Browse Source

network predicts 2 by 2 classes, refactored threshold to main

master
René Knaebel 2 years ago
parent
commit
c972963a19
4 changed files with 24 additions and 22 deletions
  1. 3
    3
      dataset.py
  2. 15
    13
      main.py
  3. 3
    3
      models.py
  4. 3
    3
      scripts/make_csv_dataset.py

+ 3
- 3
dataset.py View File

@@ -86,7 +86,7 @@ def get_cisco_features(curDataLine, urlSIPDict):
return np.zeros([numCiscoFeatures, ]).ravel()


def create_dataset_from_flows(user_flow_df, char_dict, maxLen, threshold=3, windowSize=10, use_cisco_features=False):
def create_dataset_from_flows(user_flow_df, char_dict, maxLen, windowSize=10, use_cisco_features=False):
domainLists = []
dfLists = []
print("get chunks from user data frames")
@@ -102,12 +102,12 @@ def create_dataset_from_flows(user_flow_df, char_dict, maxLen, threshold=3, wind
print("create training dataset")
return create_dataset_from_lists(
domains=domainLists, dfs=dfLists, vocab=char_dict,
maxLen=maxLen, threshold=threshold,
maxLen=maxLen,
use_cisco_features=use_cisco_features, urlSIPDIct=dict(),
window_size=windowSize)


def create_dataset_from_lists(domains, dfs, vocab, maxLen, threshold=3,
def create_dataset_from_lists(domains, dfs, vocab, maxLen,
use_cisco_features=False, urlSIPDIct=dict(),
window_size=10):
# TODO: check for hits vs vth consistency

+ 15
- 13
main.py View File

@@ -37,23 +37,24 @@ def main():
user_flow_df = dataset.get_user_flow_data()

print("create training dataset")
(X_tr, y_tr, hits_tr, names_tr, server_tr, trusted_hits_tr) = dataset.create_dataset_from_flows(
(X_tr, hits_tr, names_tr, server_tr, trusted_hits_tr) = dataset.create_dataset_from_flows(
user_flow_df, char_dict,
maxLen=maxLen, threshold=threshold, windowSize=windowSize)

pos_idx = np.where(y_tr == 1.0)[0]
neg_idx = np.where(y_tr == 0.0)[0]
maxLen=maxLen, windowSize=windowSize)
# make client labels discrete with 4 different values
# TODO: use trusted_hits_tr for client classification too
client_labels = np.apply_along_axis(lambda x: dataset.discretize_label(x, 3), 0, np.atleast_2d(hits_tr))
# select only 1.0 and 0.0 from training data
pos_idx = np.where(client_labels == 1.0)[0]
neg_idx = np.where(client_labels == 0.0)[0]
idx = np.concatenate((pos_idx, neg_idx))
# select labels for prediction
client_labels = client_labels[idx]
server_labels = server_tr[idx]

y_tr = y_tr[idx]
hits_tr = hits_tr[idx]
names_tr = names_tr[idx]
server_tr = server_tr[idx]
trusted_hits_tr = trusted_hits_tr[idx]
# TODO: remove when features are flattened
for i in range(len(X_tr)):
X_tr[i] = X_tr[i][idx]

# TODO: WTF? I don't get it...
shared_cnn = models.get_shared_cnn(len(char_dict) + 1, embeddingSize, maxLen,
domainFeatures, kernel_size, domainFeatures, 0.5)

@@ -65,8 +66,9 @@ def main():
metrics=['accuracy'])

epochNumber = 0
y_tr = np_utils.to_categorical(y_tr, 2)
model.fit(x=X_tr, y=y_tr, batch_size=128,
client_labels = np_utils.to_categorical(client_labels, 2)
server_labels = np_utils.to_categorical(server_labels, 2)
model.fit(X_tr, [client_labels, server_labels], batch_size=128,
epochs=epochNumber + 1, shuffle=True, initial_epoch=epochNumber) # ,
# validation_data=(testData,testLabel))


+ 3
- 3
models.py View File

@@ -45,9 +45,9 @@ def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, k
maxPool = GlobalMaxPooling1D()(cnn)
cnnDropout = Dropout(cnnDropout)(maxPool)
cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout)
cnnOutput = Dense(2, activation='softmax')(cnnDense)
cnnOutput1 = Dense(2, activation='softmax')(cnnDense)
cnnOutput2 = Dense(2, activation='softmax')(cnnDense)

# We define a trainable model linking the
# tweet inputs to the predictions
model = Model(inputs=inputList, outputs=cnnOutput)
return model
return Model(inputs=inputList, outputs=(cnnOutput1, cnnOutput2))

+ 3
- 3
scripts/make_csv_dataset.py View File

@@ -3,8 +3,8 @@
import joblib
import pandas as pd

datafile = joblib.load("/mnt/projekte/pmlcluster/cisco/trainData/multipleTaskLearning/currentData.joblib")
user_flows = datafile["data"]
df = pd.concat(user_flows)
df = joblib.load("/mnt/projekte/pmlcluster/cisco/trainData/multipleTaskLearning/currentData.joblib")
df = df["data"]
df = pd.concat(df)
df.reset_index(inplace=True)
df.to_csv("/tmp/rk/full_dataset.csv.gz", compression="gzip")

Loading…
Cancel
Save